To understand the expected differences in stimulation-induced dynamics based on which region is actually being stimulated, we calculated two network control statistics: the persistent modal controllability and the transient modal controllability. Intuitively, the persistent (transient) controllability is high in nodes where the addition of energy will result in large perturbations to the slow (fast) modes of the system [1]}. Typically, modal controllability is computed from the eigenvector matrix \(V = [v_{ij}]\)  of the adjacency matrix \(\mathbf {A}\) . The \(j^{\mathrm {th}}\)  mode of the system is poorly controllable from node \(i\)  if the entry for \(v_{ij}\)  is small. Modal controllability is then calculated as \(\phi _{i} = \sum _{j=1}^{N}(1 - \lambda ^{2}_{j}(A))v^{2}_{ij}\) . We adapt this discrete-time estimate to continuous-time by defining modal controllability to be \(\phi _{i} = \sum _{j=1}^{N}(1 - (e^{\lambda _{j}(A)\delta t})^{2})v^{2}_{ij}\) . Here, \(\delta t\)  is the time step of the trajectory and \(e^{\lambda _{j}(A)\delta t}\)  is the conversion from continuous to discrete eigenvalues of the system. Persistent (transient) modal controllability are computed in the same way, but using only the 10% largest (smallest) eigenvalues of the system. We explore how this cutoff affects estimates of persistent and transient controllability in the Supplement.
